A New Interacting Multiple Model Algorithm Based on the Unscented Particle Filter

Author

Xiaolong, Deng ; Pingfang, Zhou

Author_Institution

Dept. of Mech. Eng., Jiangsu Coll. of Inf. Technol., Wuxi, China

Volume

1

fYear

2009

fDate

18-20 Aug. 2009

Firstpage

419

Lastpage

422

Abstract

Combining the interacting multiple model (IMM) and the unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can adapt to targets´ high maneuvering. Particle filter can deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) may improve the approximate accuracy. Compared with other interacting multiple model algorithms in the target tracking simulations, the results demonstrate the validity of the new filtering method, that is, particle filter with the UKF proposal.

Keywords

Kalman filters; particle filtering (numerical methods); interacting multiple model algorithm; multiple model filtering algorithm; nonGaussian problem; nonlinear problem; unscented Kalman filter; unscented particle filter; Degradation; Density functional theory; Filtering algorithms; Information security; Mechanical engineering; Particle filters; Predictive models; Proposals; Target tracking; Taylor series; interacting multiple model; particle filter; target tracking; unscented particle filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Assurance and Security, 2009. IAS '09. Fifth International Conference on

Conference_Location

Xian

Print_ISBN

978-0-7695-3744-3

Type

conf

DOI

10.1109/IAS.2009.214

Filename

5284137